Sleep estimation device, sleep estimation system, wearable instrument, and sleep estimation method

ABSTRACT

A stage of sleep is easily estimated. A sleep estimation device includes a first acquisition unit configured to acquire blood flow data, a generation unit configured to generate a frequency spectrum of the blood flow data by performing frequency analysis processing on the blood flow data, and a first determination unit configured to determine a stage of sleep of a subject based on the frequency spectrum.

TECHNICAL FIELD

The present disclosure relates to estimation of a stage of sleep of asubject.

BACKGROUND OF INVENTION

Patent Document 1 discloses a technology for detecting a stage of sleep.

CITATION LIST Patent Literature

Patent Document 1: JP 2018-161432 A

SUMMARY

A sleep estimation device according to an aspect of the presentdisclosure includes a first acquisition unit configured to acquire bloodflow data indicating a blood flow of a subject, a generation unitconfigured to generate a frequency spectrum of the blood flow data byperforming frequency analysis processing on the blood flow data, and afirst determination unit configured to determine a stage of sleep of thesubject based on the frequency spectrum.

A sleep estimation device according to an aspect of the presentdisclosure includes an acquisition unit configured to acquire blood flowdata indicating a blood flow of a subject, a generation unit configuredto generate processed data indicating a result of time-frequencyanalysis processing on the blood flow data by performing wavelettransform processing or short-time Fourier transform processing, inwhich an intensity in a predetermined frequency band is relativelyemphasized compared with other frequency bands, on the blood flow data,and a determination unit configured to determine a stage of sleep of thesubject based on the processed data.

A sleep estimation method according to an aspect of the presentdisclosure includes acquiring blood flow data indicating a blood flow ofa subject, generating a frequency spectrum of the blood flow data byperforming frequency analysis processing on the blood flow data, anddetermining a stage of sleep of the subject based on the frequencyspectrum.

A sleep estimation method according to an aspect of the presentdisclosure includes acquiring blood flow data indicating a blood flow ofa subject, generating processed data indicating a result oftime-frequency analysis processing on the blood flow data by performingwavelet transform processing or short-time Fourier transform processing,in which an intensity in a predetermined frequency band is relativelyemphasized compared with other frequency bands, on the blood flow data,and determining a stage of sleep of the subject based on the processeddata.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a schematic configuration exampleof a sleep estimation system of a first embodiment.

FIG. 2 is a graph showing an example of blood flow waveform datadetected by a blood flow meter.

FIG. 3 is a graph showing an example of a frequency spectrum generatedby performing Fourier transform processing on the blood flow waveformdata.

FIG. 4 is a graph showing an example of electrocardiographic waveformdata detected by an electrocardiograph.

FIG. 5 is a graph showing an example of a frequency spectrum generatedby performing Fourier transform processing on the electrocardiographicwaveform data.

FIG. 6 is an image showing an example of a result of performing wavelettransform processing on the blood flow waveform data.

FIG. 7 is a flowchart illustrating an example of the flow of processingperformed by a sleep estimation device of the first embodiment.

FIG. 8 is a block diagram illustrating a schematic configuration exampleof a sleep estimation system of a second embodiment.

DESCRIPTION OF EMBODIMENTS

Determination (estimation) of a stage of sleep of a subject according tothe present disclosure is described below. First, the principle ofdetermining the stage of sleep of the subject according to the presentdisclosure is described. Noted that when “A to B” is described in thisspecification, it indicates “from A to B”. In this specification, bloodflow waveform data is described as an example of blood flow data usedfor determining the stage of sleep.

Principle

FIG. 2 is a graph showing an example of blood flow waveform datadetected by a blood flow meter. In FIG. 2 , the vertical axis indicatesa value proportional to the amount of blood flow per hour [units:dimensionless], and the horizontal axis indicates measurement time[units: min]. The blood flow meter can acquire raw waveform data W1 andprocessed waveform data W2 as shown in FIG. 2 as the blood flow waveformdata. The processed waveform data W2 is waveform data obtained byprocessing the raw waveform data W1 so that a peak of an R wave iseasily acquired. The processed waveform data W2 is generated by, forexample, smoothing the raw waveform data W1. In the processed waveformdata W2, the time interval between adjacent peaks (amounts of blood flowat positions indicated by inverted triangles in the drawing) indicates aheartbeat interval (RRI: R-R Interval). Also in the raw waveform dataW12 for a heartbeat shown in FIG. 4 , the time interval between adjacentpeaks (amounts of blood flow at positions indicated by the invertedtriangles in the drawing) indicates a heartbeat interval (RRI).

The blood flow meter for detecting blood flow waveform data is a sensorthat can detect blood flow waveform data indicating the blood flow of asubject by receiving scattered light generated by irradiating bloodvessels of the subject with light. The blood flow meter may include alight emitting unit for irradiating the blood vessels of the subjectwith light, and a light receiving unit for receiving the scatteredlight.

Generally, when a fluid is irradiated with a laser beam, the irradiatedlaser beam is scattered by (i) a scatterer included in the fluid andmoving with the fluid and (ii) a stationary object, such as a tube,through which the fluid flows, and scattered light is generated.Generally, the scatterer causes non-uniformity of the complex index ofrefraction in the fluid.

Due to the Doppler effect corresponding to the flow speed of thescatterer, the scattered light generated by the scatterer moving withthe fluid is subject to a wavelength shift. On the other hand, thescattered light generated by the stationary object undergoes nowavelength shift. Since scattered light generated by the scatterer andscattered light generated by the stationary object cause opticalinterference, an optical beat is observed.

The blood flow meter may be a sensor using this phenomenon. That is, theblood flow meter may be a laser Doppler blood flow meter that detects,as blood flow waveform data, an optical beat caused by scattered lightgenerated in the blood as a fluid by irradiating the blood vessels ofthe subject with a laser beam.

More specifically, a processor included in the blood flow meter mayanalyze an acquired light reception signal and calculate frequencyanalysis data indicating a signal intensity for each frequency of thelight reception signal. As an example, the processor may analyze theacquired light reception signal by using a method such as fast Fouriertransformation (FFT).

The processor may further generate blood flow waveform data indicating avariation pattern of the amount of blood flow of the subject on thebasis of the frequency analysis data. As an example, the processor maycalculate a primary moment sum X of the acquired frequency analysis dataas blood flow waveform data. More specifically, the processor maycalculate the primary moment sum X of the acquired frequency analysisdata by using the following formula. The processor may calculate theprimary moment sum X in a partial frequency band (for example, 1 to 20kHz) by using the following formula: X=Σfx×P(fx). Where, “fx” denotes afrequency and “P(fx)” denotes a value of a signal intensity at thefrequency fx.

The primary moment sum X calculated by the processor on the basis of thefrequency analysis data may be a value proportional to the amount ofblood flow of the subject. The processor may generate pattern dataindicating a variation pattern of the amount of blood flow of thesubject over time by calculating the primary moment sum X for each of aplurality of the frequency analysis data. The processor may alsogenerate blood flow waveform data by using data included in a partialfrequency band among data included in the frequency analysis data. Theprocessor can output the generated blood flow waveform data.

The blood flow waveform data may include data on at least one of cardiacoutput and the coefficient of variation of vasomotion, in addition tothe amount of blood flow. Cardiac output represents the amount of blooddelivered in one beat of the heart. Vasomotion represents acontraction-expansion movement of the blood vessel that occursspontaneously and rhythmically. The coefficient of variation ofvasomotion represents a value indicating, as a variation, the change inthe amount of blood flow occurring on the basis of the vasomotion.

The blood flow waveform data may include a pulse wave.

FIG. 3 is a graph showing an example of a frequency spectrum generatedby performing Fourier transform processing on the blood flow waveformdata as shown in FIG. 2 . The vertical axis indicates the intensity of afrequency spectrum [units: dB], and the horizontal axis indicatesfrequency [units: Hz]. Fourier transform processing is an example offrequency analysis processing, and is a process for generating afrequency spectrum of waveform data including no temporal changes.

FIG. 3 illustrates frequency spectra FW1 and FW2 corresponding to eachstage of sleep. The frequency spectrum FW1 is a frequency spectrumgenerated as a result of performing Fourier transform processing on theraw waveform data W1. The frequency spectrum FW2 is a frequency spectrumgenerated as a result of performing Fourier transform processing on theheartbeat interval (RRI) of the processed waveform data W2.

The stages of sleep can be classified into three stages, namely,wakefulness, REM sleep, and non-REM sleep. Non-REM sleep can be furtherclassified into stage 1 (N1), stage 2 (N2), and stage 3 (N3) from thelightest stage of sleep. REM sleep is sleep involving rapid eye movement(REM). Non-REM sleep is sleep involving no rapid eye movement.

This classification is performed on the basis of brain wave datadetected by an electroencephalograph attached to the subject. Brainwaves are classified into β waves, α waves, θ waves, and δ waves inascending order of wavelength. The β wave is a brain wave with afrequency of about 38 to 14 Hz, for example. The a wave is a brain wavewith a frequency of about 14 to 8 Hz, for example. The θ wave is a brainwave with a frequency of about 8 to 4 Hz, for example. The δ wave is abrain wave with a frequency of about 4 to 0.5 Hz, for example.

A person is asleep when the θ wave and the δ wave are dominant relativeto the β wave and the a wave. Here, the expression “are dominant” meansthat the percentage of a certain wave is large in the measured brainwaves. The dominant brain wave is known to periodically change in therange of the θ wave and the δ wave during sleep. When the percentage ofthe θ wave included in the brain waves is less than a predeterminedvalue, a person is in the state of REM sleep, and when the percentage ofthe θ wave is equal to or greater than a predetermined value and whenthe δ wave is dominant, a person is in the state of non-REM sleep. Stage1 represents, for example, a state in which the α wave is equal to orless than 50%, and various low-amplitude frequencies are mixed. Stage 2represents, for example, a state in which an irregular low-amplitude θwave and δ wave appear, but no high-amplitude slow wave exists. Stage 3represents, for example, a state in which a slow wave of equal to orless than 2 Hz and 75 μV is 20% or more. A state in which a slow wave ofequal to or less than 2 Hz and 75 μV is 50% or more may be referred toas stage 4.

In FIG. 3 , as the stages of sleep, wakefulness is indicated by “WK”,REM sleep is indicated by “RM”, stage 1 of non-REM sleep is indicated by“N1”, stage 2 of non-REM sleep is indicated by “N2”, and stage 3 ofnon-REM sleep is indicated by “N3”.

As shown in the frequency spectra FW1 and FW2 in FIG. 3 , a significantintensity change is recognized in a frequency band (predeterminedfrequency band) of 0.2 to 0.3 Hz in the frequency spectrum obtained fromthe subject who is in stages 2 and 3 of sleep. In other words, in thefrequency spectrum, an intensity in a first range R1, which is a part ofthe frequency band of 0.2 to 0.3 Hz, is equal to or greater than anintensity in a second range R2, other than the first range R1, by apredetermined value. Hereinafter, the intensity in the first range R1 isreferred to as a first intensity and the intensity in the second rangeR2 is referred to as a second intensity.

The first intensity may be, for example, a maximum intensity in thefrequency band of 0.2 to 0.3 Hz. The second intensity may be, forexample, a maximum intensity in the second range R2 other than the firstrange R1 (for example, about ±0.02 Hz centered on the maximum intensity,but within the frequency band of 0.2 to 0.3 Hz) including the maximumintensity. When a characteristic waveform Sh not found in an adjacentfrequency band is included in the frequency band of 0.2 to 0.3 Hz, thepredetermined value may be set, for example, by experimentation, to theextent that the inclusion of the waveform Sh can be specified. Thecharacteristic waveform Sh has, for example, an upwardly convex shapewith a somewhat broad waveform (for example, a waveform having a totalhalf width of 0.03 Hz or more). FIG. 3 illustrates an example of thefirst range R1 and the second range R2 in the frequency spectra FW1 andFW2 corresponding to stage 3.

On the other hand, in the frequency spectrum obtained from the subjectin the stages of sleep of wakefulness, REM sleep, and stage 1, nosignificant intensity change is recognized in the frequency band of 0.2to 0.3 Hz, and the characteristic waveform Sh as described above is alsonot observed.

After intensive research, the inventors have found that the subject islikely to be in stage 2 or 3 of sleep in a frequency spectrum in which asignificant intensity change (characteristic waveform Sh) is observed inthe frequency band of 0.2 to 0.3 Hz. That is, the inventors have foundthat in the frequency spectrum including the characteristic waveform Sh,the subject is likely to be in stage 2 or 3 of sleep. The inventors havealso found that a significant intensity change is observed in thefrequency band of 0.2 to 0.3 Hz especially in the frequency spectrum ofthe blood flow waveform data detected by the above-described blood flowmeter (for example, the laser Doppler blood flow meter). Based on thesefindings, the inventors have developed a sleep estimation device thatcan improve the accuracy of determining the stage of sleep of a subject.

Comparison with Electrocardiographic Waveform Data

There are differences to be described below between electrocardiographicwaveform data (electrocardiogram) detected by the electrocardiograph andblood flow waveform data detected by the blood flow meter.

FIG. 4 is a graph showing an example of the electrocardiographicwaveform data detected by the electrocardiograph. In FIG. 4 , thevertical axis indicates the intensity of a heartbeat [units: dB] and thehorizontal axis indicates measurement time [units: min]. FIG. 4illustrates raw waveform data W11 and W12 of the heartbeat as theelectrocardiographic waveform data.

FIG. 5 is a graph showing an example of a frequency spectrum generatedby performing Fourier transform processing on the electrocardiographicwaveform data. The vertical axis indicates the intensity of a frequencyspectrum [units: dB], and the horizontal axis indicates frequency[units: Hz].

FIG. 5 illustrates frequency spectra FW11 and FW12 corresponding torespective stages of sleep. The frequency spectrum FW11 is a frequencyspectrum generated as a result of performing Fourier transformprocessing on the raw waveform data W11. The frequency spectrum FW12 isfrequency spectrum generated as a result of performing Fourier transformprocessing on the heartbeat interval (RRI). The stages of sleepcorresponding to the frequency spectra FW11 and FW12 are specified onthe basis of the brain wave data acquired from the electroencephalographattached to the subject.

As illustrated in FIG. 5 , in the frequency spectra FW11 and FW12obtained by converting the electrocardiographic waveform data detectedfrom the subject who is in stages 2 and 3 of sleep, no significantintensity change (characteristic waveform Sh) is observed in thefrequency band of 0.2 to 0.3 Hz. The frequency spectrum FW12corresponding to stages 2 and 3 of sleep has an upwardly convex shape inthe frequency band of 0.2 to 0.3 Hz. However, the frequency spectrumFW12 corresponding to stage 1 of sleep has a similar shape to thefrequency spectrum FW12 corresponding to stages 2 and 3. Therefore, thefact that the frequency spectrum FW12 corresponding to stages 2 and 3has a significant intensity change in the frequency band of 0.2 to 0.3Hz is not observed.

After intensive research, the inventors have found that the significantintensity change recognized in the frequency band of 0.2 to 0.3 Hz is aphenomenon peculiar to the frequency spectrum of the blood flow waveformdata. As a result, the inventors have found that when the stage of sleepof the subject is determined using a frequency spectrum converted fromthe blood flow waveform data instead of the electrocardiographicwaveform data, the subject who is in, particularly, stage 2 or 3 ofsleep can be accurately determined with a high probability.

Frequency Band

A frequency band in which the above significant intensity change isrecognized may slightly spread depending on the blood flow meter usedand individual differences in a subject. In view of this point, thesignificant intensity change not recognized in the electrocardiographicwaveform data may be sufficiently observed in the frequency band of, forexample, 0.15 to 0.4 Hz in the frequency spectra FW1 and FW2corresponding to stage 2 or 3. The following description is given underthe assumption that the frequency band in which the above significantintensity change is observed is 0.2 to 0.3 Hz.

Wavelet Transform Processing

The above principle has been described using the frequency spectrumobtained by performing the Fourier transform processing as the frequencyanalysis processing. However, the stage of sleep of the subject may bedetermined on the basis of a frequency spectrum obtained by performingwavelet transform processing as the frequency analysis processing. Thewavelet transform processing is an example of time-frequency analysisprocessing. The time-frequency analysis processing is processing forgenerating a frequency spectrum of waveform data, including temporalchanges. The wavelet transform processing is processing for generating afrequency spectrum of waveform data by using a mother wavelet that is anarbitrary reference waveform.

The mother wavelet used in the wavelet transform processing is definedas follows. In the following equation, “t” denotes a time variable, “a”denotes a scale parameter (parameter for expanding or contracting themother wavelet in the time axis direction), and “b” denotes atranslation parameter (parameter for translating the mother wavelet inthe time axis direction).

$\begin{matrix}{{\Psi_{a,b}(t)} = {\frac{1}{\sqrt{a}}{\Psi\left( \frac{t - b}{a} \right)}}} & {{Equation}1}\end{matrix}$

A function for performing the wavelet transform processing is alsodefined as follows. In the following equation, “f(t)” indicates waveformdata and “*” indicates a conjugate complex number. By putting the motherwavelet having adjusted values of “a” and “b” into the followingequation, a frequency spectrum of waveform data can be generated.

W _(ψ)(a,b)=∫_(−∞) ^(∞)ψ^(s) _(a,b)(t)f(t)dt   Equation 2

By using the wavelet transform processing, an intensity (hereinafter,referred to as a target intensity) in the frequency band of 0.2 to 0.3Hz can be relatively emphasized compared with other frequency bands. Asdescribed in the above principle, the subject is likely to be in stage 2or 3 of sleep when a frequency spectrum having the characteristicwaveform Sh in the frequency band of 0.2 to 0.3 Hz is obtained.Accordingly, by emphasizing the target intensity using the wavelettransform processing, the probability of more accurately determiningwhether the subject is in stage 2 or 3 of sleep can be increased.

In the wavelet transform processing, a mother wavelet set to emphasizethe target intensity as described above may be used. A mother waveletwith an increased target intensity can be set by adjusting the values of“a” and “b”. Morlet may also be used as the mother wavelet. In thiscase, the scale parameter “a” represents a local angular frequencyhaving a relationship of “ω=2π/a”. Since the angular frequency “ω” canbe expressed as “ω=2πf”, the wavelet transform processing may beperformed by setting a portion of the “f (frequency)” to 0.2 to 0.3 Hz.The target intensity may be an intensity in the entire frequency band of0.2 to 0.3 Hz, or an intensity in a range (for example, the first rangeR1) which is a part of the frequency band.

As a result of the wavelet transform processing on the blood flowwaveform data, intensity change data indicating temporal changes inintensity in each frequency band within a predetermined time can begenerated. The predetermined time may be set, for example, byexperimentation, to a time for which the stage of sleep of the subjectcan be accurately determined. In the present embodiment, thepredetermined time may be set to 2.5 minutes, for example.

Unlike the Fourier transform processing, the wavelet transformprocessing can generate a frequency spectrum including temporal changesin intensity, leading to an increase in the number of data compared withthe Fourier transform processing. Generally, in the generation of alearned model to be described below, a learned model can be generated tooutput more accurate output data as the number of data is increased (thenumber of characteristics to be learned is increased). Accordingly,using intensity change data is effective when the learned model isgenerated.

FIG. 6 is an image showing an example of a result of performing wavelettransform processing on blood flow waveform data. FIG. 6 illustrates animage generated by performing the wavelet transform processing on theblood flow waveform data (raw waveform data W1) serving as a basis ofthe frequency spectrum FW1 of the stage 3 shown in FIG. 3 . The image isan example of intensity change data in which the target intensity isemphasized. Hereinafter, an image indicating the intensity change datais referred to as a wavelet image.

In FIG. 6 , the vertical axis indicates frequency [units: Hz] and thehorizontal axis indicates time [units: min]. Shading in the waveletimage in FIG. 6 indicates intensity [units: dB]. That is, the waveletimage is data indicating an intensity distribution of a frequencyspectrum in a plane defined by frequency and time.

In the wavelet image of the present embodiment, a frequency band(intensity) may be expressed by color gradation. In the wavelet image,for example, a low frequency band can be expressed by cool colors and ahigh frequency band can be expressed by warm colors. Specifically, thefrequency band may be expressed by dark blue, blue, light blue,yellowish green, pale yellowish green, yellow, orange, and red indescending order. When the intensity distribution can be visiblyrecognized in the wavelet image, the frequency band may be expressed inother colors, or may be expressed by grayscale. FIG. 6 illustrates anexample of a grayscale image of the wavelet image expressed in the abovecolors.

In the wavelet image in FIG. 6 , a first region AR1 distributed alongthe time axis at and around 0.2 Hz exhibits a higher intensity than afrequency band adjacent to the first region AR1. Specifically, in afrequency band of about 0.2 Hz±about 0.05 Hz in the first region AR1, anintensity band indicated by red is distributed along the time axis, andan intensity region indicated by orange, yellow, pale yellowish green,and yellowish green is distributed around the intensity band. In FIG. 6, reference sign 101 denotes a part of the intensity band indicated byred. Reference sign 102 denotes a part of the intensity region indicatedby orange, yellow, pale yellowish green, and yellowish green. On theother hand, in adjacent frequency bands, an intensity region indicatedby light blue, blue, and dark blue is mainly distributed, but anintensity region indicated by red, orange, and yellow is notdistributed. In FIG. 6 , reference sign 103 denotes a part of theintensity region indicated by light blue, blue, and dark blue.

As illustrated in FIG. 6 , in the wavelet image, a saw-shaped intensityband is formed along the time axis in a frequency band higher than thefrequency band of 0.2 to 0.3 Hz. In the wavelet image in FIG. 6 , thesaw-shaped intensity band is formed in the second region AR2 (frequencyband of about 0.7 Hz or higher). The second region AR2 has a lowerintensity than the first region AR1 in a frequency band of about 0.9 to1.0 Hz, and is gradually reduced in intensity toward a frequency band ofless than 0.9 Hz and a frequency band of 1.0 Hz or higher.

The saw-shaped intensity band indicates an intensity distributioncorresponding to the heartbeat. The intensity distribution has a bandshape along the time axis in a sleep state, and as the sleep becomesshallower, the band shape becomes more deformed. The intensitydistribution corresponding to the heartbeat is an intensity distributionthat is not obtained by the Fourier transform processing. By using awavelet image to generate a learned model to be described below, alearned model that considers a heartbeat can be generated.

FIG. 6 illustrates an example of the wavelet image in which the targetintensity is emphasized, but it is noted that even in a wavelet image inwhich the target intensity is not emphasized, the intensity of the firstregion AR1 is higher than the intensity in an adjacent frequency band.

Generation of Learned Model

In determining the stage of sleep of a subject, a learned model(approximator) for determining the stage of sleep of the subject can beused. The learned model is a mathematical model (neural networkincluding an input layer, a hidden layer, and an output layer) thatimitates neurons of the human cranial nervous system and is trained tobe able to determine the stage of sleep of a user. Any mathematicalmodel is used as long as it can generate a learned model capable ofdetermining the stage of sleep of the subject. The mathematical modelmay be, for example, a convolutional neural network (CNN), a recurrentneural network (RNN), or a long short term memory (LSTM).

The learning refers to adjusting the strength of connection betweenunits, a bias of the connection, and the like so that a correctoperation result is output from the output layer. In the presentembodiment, when the learning is performed, learning data is input tothe input layer. In the hidden layer, an operation based on operationdata is performed on the learning data, and an operation result in thehidden layer is output from the output layer as output data. Teacherdata and the output data are compared, and the operation data isadjusted so that an error is reduced. A learned model, in which theoperation data is adjusted by repeatedly performing the process for eachof a plurality of learning data, is generated. That is, in the presentembodiment, the learned model may be generated by so-called supervisedlearning using the learning data and the teacher data. A sleepestimation device 51 to be described below can determine the stage ofsleep of the subject by using the learned model generated in thismanner.

The learning data is data serving as an example for generating thelearned model. The learning data may be a frequency spectrum generatedfrom blood flow waveform data. In the present embodiment, a waveletimage is used. The wavelet image may be one in which a target intensityis emphasized or one in which in a target intensity is not emphasized.The learning data may have different behaviors during wakefulness andduring sleep, or may have behaviors that change according to the depthof sleep. The learning data may be various types of data (for example,frequency spectra of blood flow waveform data indicating mutuallydifferent waveforms).

The teacher data is data in which a correct answer label is associatedwith the learning data. For example, data, in which the stage of sleepof a person who has acquired blood flow waveform data is associated as acorrect answer label with a frequency spectrum serving as learning data,may be used as teacher data. As described above, the stage of sleep ofthe subject may be specified on the basis of the brain wave datadetected by the electroencephalograph. As the correct answer label, acode indicating each stage of sleep may be used. Alternatively, as thecorrect answer label, a code indicating a correct answer may be used fora specific stage of sleep (for example, stage 2 or 3), and a codeindicating an incorrect answer may be used for other stages of sleep. Inthe present embodiment, as an example of the teacher data, dataassociated as a correct answer label with a wavelet image (wavelet imagespecified as stage 2 or 3 by brain wave data) known to correspond tostage 2 or 3 may be used.

The operation data is data related to an operation for generating alearned model, including data such as an operation formula, variables(for example, bias and weight) of the operation formula, and anactivation function. The bias and the weight define the strength ofconnection between units. By adjusting the bias and the weight, theaccuracy of the learned model can be increased. As a method of adjustingthe operation data, for example, a back propagation method and agradient descent method may be employed.

First Embodiment

An example of a sleep estimation system 1 capable of determining thestage of sleep of a subject, which is constructed on the basis of theabove principle, is described below. The sleep estimation system 1 ofthe present embodiment may be a system capable of determining the stageof sleep of a subject by using the above learned model.

Sleep Stage Estimation System

FIG. 1 is a block diagram illustrating a schematic configuration exampleof the sleep estimation system 1 of the first embodiment. As illustratedin FIG. 1 , the sleep estimation system 1 includes an accelerometer 2, ablood flow meter 3, and a portable terminal 5. In the portable terminal5, as a part of the function of a control unit for comprehensivelycontrolling each member of the portable terminal 5, for example, thesleep estimation device 51 is constructed to determine the stage ofsleep of a subject by executing an application capable of determiningthe stage of sleep of the subject.

Accelerometer

The accelerometer 2 is a sensor capable of detecting an accelerationcaused by the movement of the subject. The accelerometer 2 may transmitthe detected acceleration to the sleep estimation device 51 asacceleration data by wireless or wired communication. The accelerometer2 is attached to a part of a body of the subject, such as a head or afinger, for example. A known sensor such as a frequency change typesensor, a piezoelectric sensor, a piezoresistive sensor, or a capacitivesensor may be used as the accelerometer 2.

Blood Flow Meter

The blood flow meter 3 may be a blood flow meter described in the aboveprinciple. The blood flow meter 3 may be, for example, a laser Dopplerblood flow meter. In the present embodiment, the blood flow meter 3 maytransmit the raw waveform data W1 as blood flow waveform data to thesleep estimation device 51. The blood flow meter 3 may transmit, insteadof the raw waveform data W1, the processed waveform data W2 to the sleepestimation device 51. The blood flow meter 3 need not generate theprocessed waveform data W2, and the sleep estimation device 51 maygenerate the processed waveform data W2. The blood flow meter 3 isattached to a part of the body of the subject, such as the ear, finger,wrist, arm, forehead, nose, or neck.

Portable Terminal

The portable terminal 5 may be a terminal capable of performing datacommunication with at least the accelerometer 2 and the blood flow meter3. The portable terminal 5 may be, for example, a smartphone or atablet. The portable terminal 5 is equipped with the sleep estimationdevice 51, and includes a storage 52 and a notifier 53.

The storage 52 can store programs and data to be used by the controlunit (particularly, the sleep estimation device 51). The storage 52 canstore, for example, the learned model generated as described above and athreshold value for determining whether the subject is stationary.

The notifier 53 can notify the surroundings of the portable terminal 5(for example, the subject) of various information. In the presentembodiment, the notifier 53 can notify various information according tonotification instructions from the sleep estimation device 51. Thenotifier 53 may be at least any of a sound output device for outputtingsound, a vibration device for vibrating the portable terminal 5, and adisplay device for displaying an image.

Sleep Estimation Device

The sleep estimation device 51 can determine the stage of sleep of thesubject who is wearing the accelerometer 2 and the blood flow meter 3.The sleep estimation device 51 may include a second acquisition unit 11,a second determination unit 12, a first acquisition unit 13 (acquisitionunit), a generation unit 14, a first determination unit 15(determination unit), and a notifier 16.

The second acquisition unit 11 can acquire acceleration data from theaccelerometer 2. The second determination unit 12 can determine whetherthe subject is stationary, on the basis of the acceleration dataacquired by the second acquisition unit 11. For example, when theacceleration indicated by the acceleration data is less than thethreshold value stored in the storage 52, the second determination unit12 may determine that the subject is stationary. The seconddetermination unit 12 may transmit determination result data to thegeneration unit 14.

The first acquisition unit 13 can acquire blood flow waveform data (rawwaveform data W1) from the blood flow meter 3. The generation unit 14can generate a frequency spectrum of the blood flow waveform data byperforming frequency analysis processing on the blood flow waveform dataacquired by the first acquisition unit 13. The first determination unit15 can determine the stage of sleep of the subject on the basis of thefrequency spectrum generated by the generation unit 14. In other words,the first determination unit 15 can determine the transition of thedepth of sleep of the subject on the basis of the frequency spectrum.

In the present embodiment, the generation unit 14 can generate a waveletimage by performing, as the frequency analysis processing, wavelettransform processing in which a target intensity is relativelyemphasized compared with other frequency bands. The wavelet image isprocessed data indicating the result of time-frequency analysisprocessing on the blood flow waveform data. The first determination unit15 can determine the stage of sleep of the subject on the basis of thewavelet image generated by the generation unit 14.

In the present embodiment, when the first determination unit 15determines that the characteristic waveform Sh is included in thefrequency band of 0.2 to 0.3 Hz in the frequency spectrum, the firstdetermination unit 15 can determine that the stage of sleep of thesubject is stage 2 or 3. In this case, the first determination unit 15may determine that the stage of sleep of the subject has transitionedfrom stage 1 to stage 2 or 3. On the other hand, when the firstdetermination unit 15 determines that the characteristic waveform Sh isnot included in the frequency band of 0.2 to 0.3 Hz, the firstdetermination unit 15 can determine that the stage of sleep of thesubject is a stage of sleep other than stage 2 or 3. When the firstdetermination unit 15 determines that the characteristic waveform Sh isnot included in the frequency band of 0.2 to 0.3 Hz after determiningthat the subject is in stage 2 or 3 of sleep, the first determinationunit 15 may determine that the stage of sleep of the subject hastransitioned from stage 2 or 3 to stage 1. The determination regardingwhether the characteristic waveform Sh is included can be performed by,for example, determining whether the first intensity is greater than thesecond intensity by a predetermined value or more.

In the present embodiment, the first determination unit 15 may determinethe stage of sleep of the subject by using a learned model. In thiscase, the first determination unit 15 can output the determinationresult for the stage of sleep of the subject from the output layer ofthe learned model by adding, as input data, the wavelet image generatedby the generation unit 14 to the input layer of the learned model.

As described above, the learned model is generated using, as an exampleof the teacher data, the data associated as a correct answer label withthe wavelet image known to correspond to stage 2 or 3. Therefore, byadding the wavelet image generated by the generation unit 14 to thelearned model, the first determination unit 15 can determine that thestage of sleep of the subject is stage 2 or 3 or that the stage of sleepof the subject has transitioned from stage 1 to stage 2 or 3.Particularly, when a frequency spectrum including the characteristicwaveform Sh in the frequency band of 0.2 to 0.3 Hz is added to thelearned model, the first determination unit 15 may accurately determinethat the stage of sleep of the subject is stage 2 or 3.

When the stage of sleep is estimated using the blood flow waveform data,the sleep estimation device may not be able to determine whether thesubject is falling asleep only by the blood flow waveform data. Forexample, in the case of a subject whose blood flow is stable even whenawake, a significant difference may not be observed between blood flowwaveform data during wakefulness and blood flow waveform data duringsleep, and in this case, the sleep estimation device may not be able todetermine whether the subject is falling asleep. In the presentembodiment, when the second determination unit 12 determines that thesubject is stationary, the generation unit 14 can perform thedetermination process by the first determination unit 15 in a state inwhich the subject is likely to have fallen asleep by performingfrequency analysis processing on the blood flow waveform data.

The notifier 16 can perform notification processing based on thedetermination result of the first determination unit 15. The notifier 16may transmit a notification instruction according to the notificationprocessing to the notifier 53. Thus, the notifier 53 can notify thesurroundings of the portable terminal 5 of information according to thenotification processing. The notifier 16 may include a first notifier161 and a second notifier 162.

The first notifier 161 can perform first notification processing as theabove notification processing after the first determination unit 15detects a transition from stage 1 to stage 2 or 3 and a predeterminedtime elapses. The first notification processing is a notificationprocess associated with the detection of the transition, and forexample, may be an alarm process for promoting the subject to wake up,or a process for notifying the detection of the transition. Thepredetermined time may be appropriately set by, for example, anexperiment according to the purpose of the notification. In the presentembodiment, the predetermined time may be set to a time that isestimated to make the subject more likely to wake up when measured fromthe point of transition from stage 1 to stage 2 or 3, for example. Bythe first notification processing, for example, the subject can wake upat a good awakening timing after falling asleep. The first notifier 161may perform the first notification processing when the firstdetermination unit 15 detects the transition from stage 1 to stage 2 or3.

The second notifier 162 can perform second notification processing asthe above notification processing after the first determination unit 15detects a transition from stage 2 or 3 to stage 1 and a predeterminedtime elapses. The second notification processing is a notificationprocess associated with the detection of the transition, and forexample, may be an alarm process for prompting the subject to wake up,or a process for notifying the detection of the transition. Thepredetermined time may be appropriately set by, for example, anexperiment according to the purpose of the notification. In the presentembodiment, the predetermined time may be set to a time that isestimated to make the subject more likely to wake up when measured fromthe point of transition from stage 2 or 3 to stage 1, for example. Bythe second notification processing, for example, the subject can wake upat a good awakening timing after having fallen asleep. The secondnotifier 162 may perform the second notification processing when thefirst determination unit 15 detects the transition from stage 2 or 3 tostage 1.

Processing Flow

FIG. 7 is a flowchart illustrating an example of the flow (sleepestimation method) of processing performed by the sleep estimationdevice 51. When the sleep estimation device 51 determines the stage ofsleep of the subject, after the blood flow meter 3 is attached to thesubject, the blood flow meter 3 may start detecting blood flow waveformdata.

As illustrated in FIG. 7 , in the sleep estimation device 51, the firstacquisition unit 13 can acquire blood flow waveform data from the bloodflow meter 3 (S1: first acquisition step, acquisition step). Thegeneration unit 14 can generate the frequency spectrum of the blood flowwaveform data by performing frequency analysis processing on the bloodflow waveform data. In the present embodiment, the generation unit 14can generate the wavelet image, in which the target intensity isemphasized, by performing the wavelet transform processing on the bloodflow waveform data (S2: generation step). The first determination unit15 can determine the stage of sleep of the subject on the basis of thefrequency spectrum. In the present embodiment, the first determinationunit 15 can input the wavelet image in which the target intensity isemphasized into the learned model (S3), thereby determining the stage ofsleep of the subject (S4: first determination step, determination step).

On the basis of the determination result for the stage of sleep, thefirst determination unit 15 can determine whether the stage of sleep hastransitioned from stage 1 to stage 2 or 3 (S5). When the firstdetermination unit 15 determines that the stage of sleep hastransitioned from stage 1 to stage 2 or 3 (YES at S5), the firstnotifier 161 can determine whether the predetermined time has elapsedfrom the determination (S6). When the first notifier 161 determines thatthe predetermined time has elapsed (YES at S6), the first notifier 161can perform, as the first notification processing, an alarm process forcausing the notifier 53 to emit an alarm sound, for example (S7). Uponreceiving a notification instruction from the first notifier 161, thenotifier 53 can emit an alarm sound.

When NO at S5, the procedure may return to the process of S1. When NO atS6, the process of S6 may be repeated. When the first determination unit15 determines that the stage of sleep has transitioned from stage 2 or 3to stage 1 at S5, the second notifier 162 may perform the secondnotification processing, for example, after the predetermined timeelapses.

Problem in Related Art and Effect of Sleep Estimation Device Accordingto Present Disclosure

Patent Document 1 discloses a method for detecting non-REM sleepincluding the following steps 1 to 4.

-   -   Step 1: step of generating time-series data of the beat interval        of the heart of a subject.    -   Step 2: step of setting a prescribed time length window moving        along the time axis of the time-series data and performing,        regarding each of a plurality of determination points on the        time axis, spectrum analysis on the time-series data in windows        including the determination points.    -   Step 3: step of calculating a degree of concentration of power        of heartbeat fluctuation high-frequency components from the        spectrum of each of the windows.    -   Step of determining whether sleep is non-REM sleep on the basis        of the calculated degree of concentration.

In the above method, for example, a pulse wave meter or anelectrocardiograph is used as a device for detecting the time-seriesdata of step 1.

However, handling an electroencephalograph and acquisition of brainwaves require highly specialized knowledge. In addition, attachment ofan electroencephalograph is complicated. Acquisition of brain waves isdifficult, and it is difficult for the subject to easily ascertainhis/her stage of sleep.

As described above, the inventors have found that when a significantintensity change is recognized in the frequency spectrum of 0.2 to 0.3Hz in the frequency band of the blood flow waveform data, the subject islikely to be in stage 2 or 3 of sleep.

The sleep estimation device 51 of the present disclosure can determinethe stage of sleep of the subject by using the blood flow waveform data.In comparison with the electroencephalograph, handling the blood flowmeter and acquiring the blood flow waveform data do not require highlyspecialized knowledge. In addition, the attachment of the blood flowmeter is easier than the attachment of the electroencephalograph. Thatis, the sleep estimation device 51 can relatively easily acquire theblood flow waveform data of the subject, thereby relatively easilydetermining the stage of sleep of the subject. According to the sleepestimation device 51, the subject can easily ascertain his/her stage ofsleep.

When a frequency spectrum having the above significant intensity changenot found in the electrocardiographic waveform data is obtained, thesleep estimation device 51 of the present disclosure can determine thatthe stage of sleep of the subject is stage 2 or 3. Therefore, thelikelihood that the sleep estimation device 51 can accurately estimatethe stage of sleep of the subject to be stage 2 or 3 can be increased.

Second Embodiment

Another embodiment of the present disclosure will be described below.Note that, for convenience of description, a member having the samefunction as that of a member described in the embodiments describedabove is denoted by the same reference sign, and description thereofwill not be repeated. FIG. 8 is a block diagram illustrating a schematicconfiguration example of a sleep estimation system 1A of a secondembodiment.

In the sleep estimation system 1 of the first embodiment, the portableterminal 5 can acquire the blood flow waveform data from the blood flowmeter 3 attached to the subject, for example, by wireless communication.Then, the sleep estimation device 51 built in the portable terminal 5can determine the stage of sleep of the subject on the basis of theblood flow waveform data.

On the other hand, as illustrated in FIG. 8 , in the sleep estimationsystem 1A of the second embodiment, a wearable instrument 20 may beprovided with the blood flow meter 3. In addition, in the wearableinstrument 20, the sleep estimation device 51 may be constructed as apart of the function of a control unit that comprehensively controlseach member of the wearable instrument 20. That is, in the sleepestimation system 1A, the sleep estimation device 51 may be attached tothe wearable instrument 20 together with the blood flow meter 3.Therefore, one instrument can perform the process of acquiring the bloodflow waveform data, and the process of determining the stage of sleepbased on the blood flow waveform data. Furthermore, various instruments,components, and the like required when performing wireless or wiredcommunication between two instruments are not required. The wearableinstrument 20 may be attached to the same position as the position wherethe blood flow meter 3 is attached to the subject.

Variation

In the present disclosure, the invention has been described above basedon the various drawings and examples. However, the invention accordingto the present disclosure is not limited to each embodiment describedabove. That is, the embodiments of the invention according to thepresent disclosure can be modified in various ways within the scopeillustrated in the present disclosure, and embodiments obtained byappropriately combining the technical means disclosed in differentembodiments are also included in the technical scope of the inventionaccording to the present disclosure. In other words, note that a personskilled in the art can easily make various variations or modificationsbased on the present disclosure. Note that these variations ormodifications are included within the scope of the present disclosure.

Variation of Frequency Analysis Processing

For example, the generation unit 14 may be able to generate a wavelettransform image by performing the wavelet transform processing on theblood flow waveform data, and does not necessarily have to perform thewavelet transform processing so that the target intensity is emphasized.

The generation unit 14 may also perform time-frequency analysisprocessing, other than the wavelet transform processing, on the bloodflow waveform data. The generation unit 14 may perform, for example,short-time Fourier transform processing on the blood flow waveform data.The short-time Fourier transform processing involves performing Fouriertransform processing on each of a plurality of waveform data cut along atime axis by using a window function. In this case, the generation unit14 may generate, as processed data, an image (intensity change data)similar to the wavelet image by performing short-time Fourier transformprocessing in which the target intensity is relatively emphasizedcompared with other frequency bands.

The generation unit 14 may also perform processing, other than thetime-frequency analysis processing, as the frequency analysisprocessing. The generation unit 14 may perform Fourier transformprocessing, for example. When the generation unit 14 performs Fouriertransform processing, for example, the frequency spectrum FW1 or FW2illustrated in FIG. 3 can be generated. The generation unit 14 may alsogenerate a frequency spectrum (waveform) as illustrated in FIG. 3 byperforming short-time Fourier transform processing.

In this way, the generation unit 14 can generate various frequencyspectra. Accordingly, the first determination unit 15 can determine thestage of sleep of the subject by inputting various frequency spectrainto a learned model. However, the learned model is generated using thesame kind of frequency spectrum as the frequency spectrum, which isgenerated by the generation unit 14, as learning data and teacher data.

Variation of Determination Processing of First Determination Unit

The learned model need not be stored in the storage 52 of the portableterminal 5 or the wearable instrument 20. That is, the sleep estimationsystem 1 or 1A need not determine the stage of sleep of the subject byusing the learned model.

For example, when the first determination unit 15 determines that thefirst intensity is greater than the second intensity by thepredetermined value or more in the frequency spectrum generated by thegeneration unit 14, the stage of sleep of the subject may be determinedto be stage 2 or 3. The storage 52 may store the predetermined valueinstead of the learned model.

Once the first determination unit 15 determines the stage of sleep ofthe subject by using the frequency spectrum FW1, the first determinationunit 15 may determine that the stage of sleep of the subject is stage 2or 3 when the characteristic waveform Sh can be extracted in thefrequency band of 0.2 to 0.3 Hz. In this case, the storage 52 may storea reference waveform, by which the characteristic waveform Sh can beextracted in the frequency spectrum FW1, instead of the learned model.When the first determination unit 15 determines that a waveform matchingthe reference waveform exists in the frequency band of 0.2 to 0.3 Hz inthe frequency spectrum FW1, the first determination unit 15 maydetermine that the characteristic waveform Sh can be extracted in thefrequency band. When the degree of matching between a waveform includedin the frequency band of 0.2 to 0.3 Hz and the reference waveform isequal to or greater than a threshold value set by, for example,experimentation, the first determination unit 15 may determine that thewaveform matching the reference waveform exists in the frequency band of0.2 to 0.3 Hz. On the basis of another index (for example, the degree ofchange in the slope of a waveform), the first determination unit 15 mayalso determine whether the characteristic waveform Sh is included in thefrequency band of 0.2 to 0.3 Hz. The same determination as in thefrequency spectrum FW1 may be performed for the frequency spectrum FW2.

Variation of Sleep Estimation System 1 or 1A

The accelerometer 2 may detect an acceleration caused by the movement ofthe subject, and the sleep estimation device 51 need not determine thesomnolent state of the subject on the basis of the acceleration. In thiscase, the sleep estimation system 1 or 1A need not include theaccelerometer 2, and the sleep estimation device 51 need not include thesecond acquisition unit 11 and the second determination unit 12.

Example of Software Implementation

A control block of the sleep estimation device 51 may be implemented bya logic circuit (hardware) formed in an integrated circuit (IC chip) orthe like, or may be implemented by software.

In the latter case, the sleep estimation device 51 includes a computerthat executes instructions of a program that is software forimplementing each function. The computer includes, for example, at leastone processor (control device) and at least one computer-readablerecording medium storing the above program. Then, in the computer, theprocessor reads the above program from the recording medium and executesthe read program to achieve the object of the present disclosure. As theprocessor, a central processing unit (CPU) can be used, for example. Asthe recording medium, a “non-transitory tangible medium” such as, forexample, a read only memory (ROM), a tape, a disk, a card, asemiconductor memory, a programmable logic circuit, and the like can beused. Additionally, a random access memory (RAM) for loading the aboveprogram may be further provided. The above program may be supplied tothe computer via any transmission medium (communication network,broadcast wave, and the like) capable of transmitting the program.Further, one aspect of the present disclosure may be implemented in theform of data signals embedded in a carrier wave in which the aboveprogram is embodied by electronic transmission.

REFERENCE SIGNS

-   -   1, 1A Sleep estimation system    -   3 Blood flow meter    -   11 Second acquisition unit    -   12 Second determination unit    -   13 First acquisition unit (acquisition unit)    -   14 Generation unit    -   15 First determination unit (determination unit)    -   20 Wearable instrument    -   51 Sleep estimation Device    -   161 First notifier    -   162 Second notifier

1. A sleep estimation device comprising: a first acquisition unitconfigured to acquire blood flow data indicating a blood flow of asubject; a generation unit configured to generate a frequency spectrumof the blood flow data by performing frequency analysis processing onthe blood flow data; and a first determination unit configured todetermine a stage of sleep of the subject based on the frequencyspectrum.
 2. The sleep estimation device according to claim 1, whereinwhen the stage of sleep in non-REM sleep is classified into stages 1 to3 comprising a stage 1, a stage 2 and a stage 3, in an order from alightest stage of sleep, the first determination unit is configured todetermine that the stage of sleep of the subject has transitioned fromthe stage 1 to the stage 2 or the stage 3, or to determine that thestage of sleep of the subject has transitioned from the stage 2 or thestage 3 to the stage 1, based on the frequency spectrum.
 3. The sleepestimation device according to claim 2, further comprising: a firstnotifier configured to perform first notification processing when thefirst determination unit determines that the stage of sleep of thesubject has transitioned from the stage 1 to the stage 2 or the stage 3,or after the first determination unit determines that the stage of sleepof the subject has transitioned from the stage 1 to the stage 2 or thestage 3 and a predetermined time has elapsed.
 4. The sleep estimationdevice according to claim 3, further comprising: a second notifierconfigured to perform second notification processing when the firstdetermination unit determines that the stage of sleep of the subject hastransitioned from the stage 2 or the stage 3 to the stage 1, or afterthe first determination unit determines that the stage of sleep of thesubject has transitioned from the stage 2 or the stage 3 to the stage 1and the predetermined time has elapsed.
 5. The sleep estimation deviceaccording to claim 2, wherein when a first intensity in a first range asa part of a predetermined frequency band in the frequency spectrum isgreater by a predetermined value or more than a second intensity in asecond range other than the first range, the first determination unit isconfigured to determine that the stage of sleep of the subject hastransitioned from the stage 1 to the stage 2 or the stage
 3. 6. Thesleep estimation device according to claim 5, wherein the predeterminedfrequency band is from 0.15 Hz to 0.4 Hz.
 7. The sleep estimation deviceaccording to claim 2, wherein the generation unit is configured togenerate, as the frequency spectrum, intensity change data indicating atemporal change in intensity in each frequency band within apredetermined time by performing time-frequency analysis processing asthe frequency analysis processing, and the first determination unit isconfigured to add the intensity change data generated by the generationunit to a learned model, the learned model learned using teacher data inwhich a correct answer label is associated with the intensity changedata known to correspond to the stage 2 or the stage 3, therebydetermining that the stage of sleep of the subject has transitioned fromthe stage 1 to the stage 2 or the stage
 3. 8. The sleep estimationdevice according to claim 1, further comprising: a second acquisitionunit configured to acquire acceleration data indicating an accelerationcaused by a movement of the subject; and a second determination unitconfigured to determine whether the subject is stationary based on theacceleration data, wherein when the second determination unit configuredto determine that the subject is stationary, the generation unitperforms the frequency analysis processing.
 9. A sleep estimation devicecomprising: an acquisition unit configured to acquire blood flow dataindicating a blood flow of a subject; a generation unit configured togenerate processed data indicating a result of time-frequency analysisprocessing on the blood flow data by performing wavelet transformprocessing or short-time Fourier transform processing, in which anintensity in a predetermined frequency band is relatively emphasizedcompared with other frequency bands, on the blood flow data; and adetermination unit configured to determine a stage of sleep of thesubject based on the processed data.
 10. The sleep estimation deviceaccording to claim 9, wherein the generation unit is configured togenerate, as the processed data, intensity change data indicating atemporal change in intensity in each frequency band within apredetermined time, and when a stage of sleep in non-REM sleep isclassified into stages 1 to 3 comprising a stage 1, a stage 2 and astage 3, in an order from a lightest stage of sleep, the determinationunit is configured to add the intensity change data generated by thegeneration unit to a learned model, the learned model learned usingteacher data in which a correct answer label is associated with theintensity change data known to correspond to the stage 2 or the stage 3,thereby determining that the stage of sleep of the subject hastransitioned from the stage 1 to the stage 2 or the stage
 3. 11. A sleepestimation system comprising: the sleep estimation device according toclaim 1; and a blood flow meter that detects the blood flow data byreceiving scattered light generated by irradiating a blood vessel of thesubject with light.
 12. A wearable instrument comprising: the sleepestimation device according to claim 1; and a blood flow meter thatdetects the blood flow data by receiving scattered light generated byirradiating a blood vessel of the subject with light.
 13. A sleepestimation method comprising: acquiring blood flow data indicating ablood flow of a subject; generating a frequency spectrum of the bloodflow data by performing frequency analysis processing on the blood flowdata; and determining a stage of sleep of the subject based on thefrequency spectrum.
 14. A sleep estimation method comprising: acquiringblood flow data indicating a blood flow of a subject; generatingprocessed data indicating a result of time-frequency analysis processingon the blood flow data by performing wavelet transform processing orshort-time Fourier transform processing, in which an intensity in apredetermined frequency band is relatively emphasized compared withother frequency bands, on the blood flow data; and determining a stageof sleep of the subject based on the processed data.